There are many factors affecting students’ grades, which make the prediction of students’ grades present high dimensional and nonlinear characteristics. Therefore, the traditional method has a large error in the prediction results, which is difficult to meet the practical needs. With the rapid development of artificial neural network (Ann), the deep cycle neural network algorithm based on Ann provides a new approach for student achievement prediction. In order to further improve the accuracy of student achievement prediction, this paper proposes a performance prediction model based on deep cycle neural network algorithm. First, principal component analysis is used for data dimensionality reduction processing of the established student writing evaluation system, and the first five principal components are extracted. Then, these principal components are taken as the input of the neural network to construct a three-layer neural network prediction model. The experimental results show that, compared with the single RBF neural network and BP neural network, the prediction model under deep cycle neural network is simple in structure, fast in convergence and 21.6% higher in prediction accuracy, which verifies the effectiveness of the model proposed in this paper.
With the acceleration of the application of information technology in Colleges and universities in China, the efficiency of higher education is constantly improving. The focus of teaching management in Colleges and universities is to continuously improve the teaching level of colleges and universities, and the key is to strengthen the management of students’ performance. Performance warning is a form of student performance management. In recent years, data mining technology is more and more mature, and its application is also very wide. Many students have applied data mining technology to university management. In this paper, we apply data mining technology to college students’ performance early warning, and use Apriori algorithm in association rules to design and build college students’ performance early warning platform, and select two classes of students as the research object to verify. In this study, we choose the English course scores of two classes as the test data, and define the performance warning, which is based on the score below 60. The results show that six students in class a will be subject to performance warning, while seven students in class B will be subject to performance warning. In addition, the performance early warning platform designed by this method, the early warning accuracy rate is as high as 92.85%, the accuracy rate is high, has certain application advantages.
Abstract. In the bridge design across the river, the cable-stayed bridge has become a very competitive bridge type. As the bridge instability occurred, the cable-stayed bridge instability problem is also of concern. In recent years, in order to meet the needs of production and life, multi-tower cable-stayed bridge has also emerged. In the multi-tower cable-stayed bridge due to the increase in the tower, the tower is not easy to control the displacement, making the already flexible structure of the cable-stayed bridge becomes softer. The possibility of instability is increased, so it is necessary to study the stability of multi-tower cable-stayed bridge. The first part introduces the research history of bridge stability analysis at home and abroad, and points out that many bridges are damaged due to instability, and the necessity of stability analysis is expounded. Then we introduce the nonlinear factors that affect the stability of the cable -stayed bridge, which introduces the causes of these factors, the mathematical calculation methods, and how to eliminate these unfavorable factors. Finally, the method of stability analysis of cable -stayed bridge is put forward, which is divided into simplified method and accurate calculation method.
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